{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=Crimson size=6 face=\"宋体\" align=\"center\">k均值聚类算法（k-means clustering algorithm）</font>     \n",
    "--------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[KMEANS交互可視化](https://www.naftaliharris.com/blog/visualizing-k-means-clustering/)\n",
    "\n",
    "kmeans算法是无监督的聚类算法\n",
    "\n",
    "以二维模拟样本为例，使用Kmeans算法聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "import ipywidgets as widgets\n",
    "import pylab\n",
    "import os\n",
    "\n",
    "from dataset_producer import Dataset,rand_data\n",
    "\n",
    "FIG_SIZE=(8,8)\n",
    "TEMP_FILE='tempSet.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入原始数据(import the dataset)\n",
    "TODO:\n",
    "添加样本点更多、数据分布不同的数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a6ad095ef9343909aca59fb2c1521ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=100, continuous_update=False, description='生成数据个数', max=10000, min=10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c698d768cab241a6b14fcbbc7f1bde53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='生成随机数据集', style=ButtonStyle(), tooltip='Click me')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae24062820b24eec900de368ed544a4c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='选择原有数据集（生成的数据集为tempset.txt）:', options=('testdata/barsSet.txt', 'testdata/blobSet.txt', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36db99e578c747428684ed26fcf7a615",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据集存放位置\n",
    "DATA_DIR=\"testdata/\"\n",
    "all_data_path=[]\n",
    "for root, dirs, files in os.walk(DATA_DIR):\n",
    "    for file in files:\n",
    "        all_data_path.append(os.path.join(root,file))\n",
    "        \n",
    "#可交互文件选择模块\n",
    "iw_filename=widgets.Dropdown(\n",
    "    options=all_data_path,\n",
    "    description='选择原有数据集（生成的数据集为%s）:'%TEMP_FILE,\n",
    "    disabled=False,\n",
    "    \n",
    ")\n",
    "\n",
    "w_gen_button=widgets.Button(\n",
    "    description='生成随机数据集',\n",
    "    disabled=False,\n",
    "    button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    tooltip='Click me',\n",
    "    icon=''\n",
    ")\n",
    "\n",
    "w_output=widgets.Output()\n",
    "\n",
    "w_rand_size=widgets.IntSlider(\n",
    "    value=100,\n",
    "    min=10,\n",
    "    max=10000,\n",
    "    continuous_update=False,\n",
    "    description='生成数据个数'\n",
    ")\n",
    "\n",
    "def gen_rand_data(b):\n",
    "    \"\"\"\n",
    "    点击按钮生成随机数据集，写入testdata，并展示\n",
    "    :param b:无意义\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    new_dataset=rand_data(n=w_rand_size.value,d=2)\n",
    "    new_dataset.path='testdata/'+TEMP_FILE\n",
    "    new_dataset.write_data()\n",
    "    with w_output:\n",
    "        w_output.clear_output()\n",
    "        new_dataset.show_data()\n",
    "\n",
    "def choose_show(change):\n",
    "    \"\"\"\n",
    "    选择原有数据集，展示原始数据\n",
    "    :param b: 无意义\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    new_dataset=Dataset(path=change['new'])\n",
    "    with w_output:\n",
    "        w_output.clear_output()\n",
    "        new_dataset.show_data()\n",
    "\n",
    "iw_filename.observe(choose_show,names='value')\n",
    "w_gen_button.on_click(gen_rand_data)\n",
    "display(w_rand_size,w_gen_button,iw_filename,w_output)\n",
    "# interact(show_base_data,filename=iw_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 探索数据(explore the data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae24062820b24eec900de368ed544a4c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='选择原有数据集（生成的数据集为tempset.txt）:', options=('testdata/barsSet.txt', 'testdata/blobSet.txt', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48153e3455cf4d1b96929b12c476ce17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "对于给定数据集进行相关处理并展示\n",
    "\"\"\"\n",
    "w_draw_output=widgets.Output()\n",
    "\n",
    "def choose_draw(change):\n",
    "    \"\"\"\n",
    "    数据集选择流程\n",
    "    用户通过下拉菜单选择数据集\n",
    "    进行相关处理并展示\n",
    "    :param filename:widget传入的文件名\n",
    "    \"\"\"\n",
    "    filename=change['new']\n",
    "    pylab.rcParams['figure.figsize'] = FIG_SIZE\n",
    "    new_dataset=Dataset(path=filename)\n",
    "    with w_draw_output:\n",
    "        w_draw_output.clear_output()\n",
    "        new_dataset.draw_data()\n",
    "\n",
    "iw_filename.observe(choose_draw,names='value')\n",
    "display(iw_filename,w_draw_output)\n",
    "# ow_data_shape=widgets.Output().append_stdout(\"该数据集有%d个%d维数据\"%(np_data.shape[0],np_data.shape[1]))\n",
    "# ow_draw_data=widgets.interactive_output(choose,{'filename':iw_filename})\n",
    "# widgets.HBox([widgets.VBox([filename_widget,ow_data_shape,ow_draw_data]),ow_base_data])\n",
    "# interact(choose_draw,filename=iw_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Kmeans算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始化聚类中心\n",
    "\n",
    "TODO:\n",
    "1.更新K值全部初始化中心点\n",
    "2.按钮添加一个随机中心点\n",
    "3.输入坐标添加中心点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def plot_data(x,idx,K,cent,centroids,cnt):\n",
    "    m=x.shape[0]\n",
    "    tmp_idx=[]\n",
    "    color_list=['r','g','y','b','m','orange']\n",
    "    tmp_idx=idx[:,0].tolist() #要把numpyfloat64转换为list int\n",
    "    for i in range(m):\n",
    "        plt.scatter(x[i,0],x[i,1],marker='x',color=color_list[int(tmp_idx[i])])\n",
    "\n",
    "   # plt.scatter(centroids[:,0],centroids[:,1],marker='o',color = 'black',linewidths=10)\n",
    "\n",
    "    for k in range(K):\n",
    "        plt.scatter(centroids[k,0],centroids[k,1],marker='o',color = color_list[k],linewidths=10)\n",
    "        plt.plot(np.array(cent[\"%d\"%(k)])[:,0],np.array(cent[\"%d\"%(k)])[:,1],\"b-o\",color=color_list[k])\n",
    "    plt.xlabel('X1')\n",
    "    plt.ylabel('X2')\n",
    "    plt.title(\"figure %d\"%(cnt))\n",
    "    plt.subplots_adjust(wspace=0)\n",
    "    plt.savefig(\"figure %d.jpg\"%(cnt),dpi=300)\n",
    "    plt.show()\n",
    "\n",
    "def randCentroids(x:np.array,k:int)->np.array:\n",
    "    \"\"\"\n",
    "    随机初始化centroids ,随机选择k个样本点作为质心\n",
    "    :param x:numpy_data\n",
    "    :param k:随机选取k个\n",
    "    :return:选取的中心点\n",
    "    \"\"\"\n",
    "    m,n = x.shape # m=80个数据，n=2维\n",
    "    centroids = np.zeros((k,n)) #产生k个 [0 0]的数据\n",
    "    randIndex = np.random.choice(m,k) #从m个数据中选k个randindex\n",
    "    centroids = x[randIndex]\n",
    "    return centroids\n",
    "\n",
    "#可交互K值选择\n",
    "# K_widget=widgets.IntSlider(value=4,min=1, max=6, step=1, continuous_update=False)\n",
    "# interact(randCentroids,k=K_widget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算距离\n",
    "def computeDistance(A,B):\n",
    "    return np.sqrt(np.sum(np.square(A-B)))\n",
    "\n",
    "#计算x的均值，改变centroids\n",
    "def change_centroids(x,idx,K):\n",
    "    m,n = x.shape\n",
    "    centroids = np.zeros((K,n))\n",
    "    for i in range(K):\n",
    "        index = np.where(idx[:,0].ravel() == i)\n",
    "        centroids[i] = np.mean(x[index],axis=0)\n",
    "    return centroids\n",
    "\n",
    "#为数据集x找到最近的质心,直到质心不变为止   \n",
    "def kmeans(x,K):\n",
    "    #1.初始化质心\n",
    "    centroids = randCentroids(x,K)\n",
    "    m = x.shape[0]\n",
    "    idx = np.zeros((x.shape[0],2)) #记录x对应的质心的下标 + 平方误差\n",
    "    clusterchanged = True #记录分类是否变化\n",
    "    cent = {} #用字典，来存储各个质心的变化\n",
    "    cnt=0 #记录分配点的次数\n",
    "    \n",
    "    # 初始化质心字典\n",
    "    for k in range(K):\n",
    "        cent[\"%d\"%(k)] = []\n",
    "        cent[\"%d\"%(k)].append(centroids[k,:].tolist())\n",
    "        \n",
    "    while clusterchanged:\n",
    "        \n",
    "        clusterchanged = False\n",
    "        #2.为每个样本点分配质心\n",
    "        for i in range(m):\n",
    "            minDist = np.inf\n",
    "            minIndex = -1\n",
    "            for j in range(K):\n",
    "                distance = computeDistance(x[i,:],centroids[j,:])\n",
    "                if distance<minDist:\n",
    "                    minDist = distance\n",
    "                    minIndex = j\n",
    "            if idx[i,0] != minIndex:\n",
    "                clusterchanged = True\n",
    "            idx[i,:] = minIndex, minDist**2\n",
    "        #分配完点之后的情况输出一下\n",
    "        cnt=cnt+1\n",
    "        plot_data(x,idx,K,cent,centroids,cnt)\n",
    "        #3.更新质心的位置\n",
    "        centroids = change_centroids(x,idx,K)\n",
    "        # 4.更新质心字典\n",
    "        for k in range(K):\n",
    "            cent[\"%d\"%(k)].append(centroids[k,:].tolist())\n",
    "    plot_data(x,idx,K,cent,centroids,cnt)\n",
    "    \n",
    "    return centroids,idx,cent,cnt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25425cd5a30947e8a30fa4073fcd2963",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=4, continuous_update=False, max=6, min=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data_set=Dataset(path=iw_filename.value)\n",
    "K_widget=widgets.IntSlider(value=4,min=1, max=6, step=1, continuous_update=False)\n",
    "display(K_widget)\n",
    "pylab.rcParams['figure.figsize'] = FIG_SIZE\n",
    "centroids,idx,cent,cnt = kmeans(new_data_set.data,K_widget.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-26-63b40e528d93>, line 20)",
     "output_type": "error",
     "traceback": [
      "\u001B[1;36m  File \u001B[1;32m\"<ipython-input-26-63b40e528d93>\"\u001B[1;36m, line \u001B[1;32m20\u001B[0m\n\u001B[1;33m    out.\u001B[0m\n\u001B[1;37m        ^\u001B[0m\n\u001B[1;31mSyntaxError\u001B[0m\u001B[1;31m:\u001B[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def choose_display(n):\n",
    "    pylab.rcParams['figure.figsize'] = FIG_SIZE\n",
    "    a=plt.imread(\"figure %d.jpg\"%(n))\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(a)\n",
    "\n",
    "n_widget=widgets.IntSlider(min=1,max=cnt,description='迭代次数')\n",
    "play_widget = widgets.Play(\n",
    "    interval=2000,\n",
    "    value=1,\n",
    "    min=1,\n",
    "    max=cnt,\n",
    "    step=1,\n",
    "    description=\"Press play\",\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "widgets.jslink((play_widget, 'value'), (n_widget, 'value'))\n",
    "out = widgets.interactive_output(choose_display, {'n': n_widget})\n",
    "# widgets.VBox([widgets.HBox([play_widget,n_widget]), out])\n",
    "display(play_widget,n_widget,out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Kmeans算法的局限性\n",
    "\n",
    "TODO:\n",
    "1.依赖初始聚类中心\n",
    "2.K值的选择\n",
    "3.适用的数据类型"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}